A Negative-Aware and Rating-Integrated Recommendation Algorithm Based on Bipartite Network Projection

  • Fengjing Yin
  • Xiang Zhao
  • Guangxin Zhou
  • Xin Zhang
  • Shengze Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8506)


Bipartite network projection method has been recently employed for personal recommendation. It constructs a bipartite network between users and items. Treating as resource in the network user taste for items, it allocates the resource via links between user nodes and item nodes. However, the taste model employed by existing algorithms cannot differentiate “dislike” and “unrated” cases implied by user ratings. Moreover, the distribution of resource is solely based on node degrees, ignoring the different transfer rates of the links. To enhance the performance, this paper devises a negative-aware and rating-integrated algorithm on top of the baseline algorithm. It enriches the current user taste model to encompass “like”, “dislike” and “unrated” information from users. Furthermore, in the resource distribution stage, we propose to initialize the resource allocation according to user ratings, which also determines the resource transfer rates on links afterward. Extensive experiments conducted on real data validate the effectiveness of the proposed algorithm.


User Rating Resource Distribution Collaborative Filter Baseline Algorithm Bipartite Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Marko, B., Yoav, S.: Fab: Content-based Collaborative Recommendation. Comm. of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  2. 2.
    Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R.: GroupLens: Applying Collaborative Filtering to Usenet News. Comm. of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  3. 3.
    Khoshneshin, M., Street, N.W.: Collaborative Filtering via Euclidean Embedding. In: ACM RecSys 2010, Barcelona, Spain, September 26-30 (2010)Google Scholar
  4. 4.
    Koren, Y.: Collaborative Filtering with Temporal Dynamics. In: ACM KDD 2009, Paris, France, June 28-July 1 (2009)Google Scholar
  5. 5.
    Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  6. 6.
    Shi, Y., Larson, M., Hanjalic, A.: Mining Contextual Movie Similarity with Matrix Factorization for Context-Aware Recommendation. ACM TIST 04(01), 1601–1619 (2013)Google Scholar
  7. 7.
    Liu, H., Maes, P.: InterestMap: Harvesting Social Network Profiles for Recommendations. In: IUI 2005, San Diego, California, USA (January 9, 2005)Google Scholar
  8. 8.
    Zhou, T., Ren, J., Medo, M., Zhang, Y.: Bipartite Network Projection and Personal Recommendation. Phys. Rev. E 76(4), 46115 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Blattner, M., Yu, Y.: Heat Conduction Process on Community Networks as a Recommendation Model. Phys. Rev. Lett. 99, 154301 (2007)CrossRefGoogle Scholar
  10. 10.
    Zhang, Y., Medo, M., Ren, J., Zhou, T., Li, T., Yang, F.: Recommendation Model Based on Opinion Diffusion, Europhys. Europhys. Lett. 80(2008), 68003 (2008)MathSciNetGoogle Scholar
  11. 11.
    Liu, J., Zhou, T., Wang, B., Zhang, Y.: Effects of User’s Tastes on Personalized Recommendation. Int. J. Mod. Phys. C 20, 1925–1932 (2009)CrossRefGoogle Scholar
  12. 12.
    Zhou, T., Jiang, L., Su, R., Zhang, Y.: Effect of Initial Configuration on Network-Based Recommendation. Europhys. Lett. 81(2008), 58004 (2008)CrossRefGoogle Scholar
  13. 13.
    Xia, J., Wu, F., Xie, C., Tu, J.: INBI: An Improved Network-Based Inference Recommendation Algorithm, In: IEEE NAS 2012, June 28-30 (2012)Google Scholar
  14. 14.
    Fernandez, Y.B., Arias, J.P., Solla, A.G., Cabrer, M.R., Nores, M.L.: Providing Entertainment by Content-based Filtering and Semantic Reasoning in Intelligent Recommender Systems. IEEE Tconsum. Electr. 54(2), 727–735 (2008)CrossRefGoogle Scholar
  15. 15.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems. In: Proceedings of ICCIT, April 2-4 (2002)Google Scholar
  16. 16.
    Liu, F., Lee, H.J.: Use of Social Network Information to Enhance Collaborative Filtering Performance. Expert Syst. Appl. 37(7), 4772–4778 (2010)CrossRefGoogle Scholar
  17. 17.
    Liu, J., Zhou, T., Che, H., Wang, B., Zhang, Y.: Effects of High-order Correlations on Personalized Recommendations for Bipartite Networks. Physica A 389(2010), 881–886 (2010)CrossRefGoogle Scholar
  18. 18.
    Liu, J., Zhou, T., Guo, Q.: Information Filtering via Biased Heat Conduction. Phys. Rev. E 84, 37101 (2011)CrossRefGoogle Scholar
  19. 19.
    Quan, J., Fu, Y.: A Novel Collaborative Filtering Algorithm Based on Bipartite Network Projection. JDCTA 6(1), 391–397 (2012)CrossRefGoogle Scholar
  20. 20.
    Sawant, S.: Collaborative Filtering using Weighted Bipartite Graph Projection: A Recommendation System for Yelp. In: CS224W: Social and Information Network Analysis (December 10, 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fengjing Yin
    • 1
  • Xiang Zhao
    • 1
  • Guangxin Zhou
    • 1
  • Xin Zhang
    • 1
  • Shengze Hu
    • 1
  1. 1.National University of Defense TechnologyChangshaP.R.China

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